Using ant colony optimization for routing in microprocesors

Power consumption is an important constraint on VLSI systems. With the advancement in technology, it is now possible to pack a large range of functionalities into VLSI devices. Hence it is important to find out ways to utilize these functionalities with optimized power consumption. This work focuses on curbing power consumption at the design stage. This work emphasizes minimizing active power consumption by minimizing the load capacitance of the chip. Capacitance of wires and vias can be minimized using Ant Colony Optimization (ACO) algorithms. ACO provides a multi agent framework for combinatorial optimization problems and hence is used to handle multiple constraints of minimizing wire-length and vias to achieve the goal of minimizing capacitance and hence power consumption. The ACO developed here is able to achieve an 8% reduction of wire-length and 7% reduction in vias thereby providing a 7% reduction in total capacitance, compared to other state of the art routers

Novel Metric for Load Balance and Congestion Reducing in Network on-Chip

The Network-on-Chip (NoC) is an alternative pattern that is considered as an emerging technology for distributed embedded systems. The traditional use of multi-cores in computing increase the calculation performance; but affect the network communication causing congestion on nodes which therefore decrease the global performance of the NoC. To alleviate this problematic phenomenon, several strategies were implemented, to reduce or prevent the occurrence of congestion, such as network status metrics, new routing algorithm, packets injection control, and switching strategies. In this paper, we carried out a study on congestion in a 2D mesh network, through various detailed simulations. Our focus was on the most used congestion metrics in NoC. According to our experiments and performed simulations under different traffic scenarios, we found that these metrics are less representative, less significant and yet they do not give a true overview of reading within the NoC nodes at a given cycle. Our study shows that the use of other complementary information regarding the state of nodes and network traffic flow in the design of a novel metric, can really improve the results. In this paper, we put forward a novel metric that takes into account the overall operating state of a router in the design of adaptive XY routing algorithm, aiming to improve routing decisions and network performance. We compare the throughput, latency, resource utilization, and congestion occurrence of proposed metric to three published metrics on two specific traffic patterns in a varied packets injection rate. Our results indicate that our novel metric-based adaptive XY routing has overcome congestion and significantly improve resource utilization through load balancing; achieving an average improvement rate up to 40 % compared to adaptive XY routing based on the previous congestion metrics

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

Improving peer review with ACORN : Ant Colony Optimization algorithm for Reviewer\u27s Network

Peer review, our current system for determining which papers to accept and which to reject by journals and conferences, has limitations that impair the quality of scientific communication. Under the current system, reviewers have only a limited amount of time to devote to evaluating papers and each paper receives an equal amount of attention regardless of how good the paper is. We propose to implement a new system for conference peer review based on ant colony optimization (ACO) algorithms. In our model, each reviewer has a set of ants that goes out and finds articles. The reviewer assesses the paper that the ant brings according to the criteria specified by the conference organizers and the ant deposits pheromone that is proportional to the quality of the review. Each subsequent ant then samples the pheromones and probabilistically selects the next article based on the strength of the pheromones. We used an agent-based model to determine if an ACO-based paper selection system will direct reviewers attention to the best articles and if the average quality of papers increases with each round of reviews. We also conducted an experiment in conjunction with the 2011 UNM Computer Science Graduate Student Association conference and compared the results with our simulation. To assess the usefulness of our approach, we compared our algorithm to a greedy algorithm that always takes the best un-reviewed paper and a latent factor analysis recommender-based system. We found that the ACO-based algorithm was better than either of the greedy or recommender algorithms at directing users\u27 attention to the better papers

CMOS VLSI Layout and Verification of a SIMD Computer

A CMOS VLSI layout and verification of a 3 x 3 processor parallel computer has been completed. The layout was done using the MAGIC tool and the verification using HSPICE. Suggestions for expanding the computer into a million processor network are presented. Many problems that might be encountered when implementing a massively parallel computer are discussed

HEDCOS: High Efficiency Dynamic Combinatorial Optimization System using Ant Colony Optimization algorithm

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDynamic combinatorial optimization is gaining popularity among industrial practitioners due to the ever-increasing scale of their optimization problems and efforts to solve them to remain competitive. Larger optimization problems are not only more computationally intense to optimize but also have more uncertainty within problem inputs. If some aspects of the problem are subject to dynamic change, it becomes a Dynamic Optimization Problem (DOP). In this thesis, a High Efficiency Dynamic Combinatorial Optimization System is built to solve challenging DOPs with high-quality solutions. The system is created using Ant Colony Optimization (ACO) baseline algorithm with three novel developments. First, introduced an extension method for ACO algorithm called Dynamic Impact. Dynamic Impact is designed to improve convergence and solution quality by solving challenging optimization problems with a non-linear relationship between resource consumption and fitness. This proposed method is tested against the real-world Microchip Manufacturing Plant Production Floor Optimization (MMPPFO) problem and the theoretical benchmark Multidimensional Knapsack Problem (MKP). Second, a non-stochastic dataset generation method was introduced to solve the dynamic optimization research replicability problem. This method uses a static benchmark dataset as a starting point and source of entropy to generate a sequence of dynamic states. Then using this method, 1405 Dynamic Multidimensional Knapsack Problem (DMKP) benchmark datasets were generated and published using famous static MKP benchmark instances as the initial state. Third, introduced a nature-inspired discrete dynamic optimization strategy for ACO by modelling real-world ants’ symbiotic relationship with aphids. ACO with Aphids strategy is designed to solve discrete domain DOPs with event-triggered discrete dynamism. The strategy improved inter-state convergence by allowing better solution recovery after dynamic environment changes. Aphids mediate the information from previous dynamic optimization states to maximize initial results performance and minimize the impact on convergence speed. This strategy is tested for DMKP and against identical ACO implementations using Full-Restart and Pheromone-Sharing strategies, with all other variables isolated. Overall, Dynamic Impact and ACO with Aphids developments are compounding. Using Dynamic Impact on single objective optimization of MMPPFO, the fitness value was improved by 33.2% over the ACO algorithm without Dynamic Impact. MKP benchmark instances of low complexity have been solved to a 100% success rate even when a high degree of solution sparseness is observed, and large complexity instances have shown the average gap improved by 4.26 times. ACO with Aphids has also demonstrated superior performance over the Pheromone-Sharing strategy in every test on average gap reduced by 29.2% for a total compounded dynamic optimization performance improvement of 6.02 times. Also, ACO with Aphids has outperformed the Full-Restart strategy for large datasets groups, and the overall average gap is reduced by 52.5% for a total compounded dynamic optimization performance improvement of 8.99 times

Runtime Adaptive System-on-Chip Communication Architecture

The adaptive system provides adaptivity both in the system-level and in the architecture-level. The system-level adaptation is provided using a runtime application mapping. The architecture-level adaptation is implemented by using several novel methodologies to increase the resource utilization of the underlying silicon fabric, i.e. sharing the Virtual Channel Buffers among different output ports. To achieve successful runtime adaptation, a runtime observability infrastructure is included